TANE

TANE: An efficient algorithm for discovering functional and approximate dependencies. environments. The discovery of functional dependencies from relations is an important database analysis technique. We present TANE, an efficient algorithm for finding functional dependencies from large databases. TANE is based on partitioning the set of rows with respect to their attribute values, which makes testing the validity of functional dependencies fast even for a large number of tuples. The use of partitions also makes the discovery of approximate functional dependencies easy and efficient and the erroneous or exceptional rows can be identified easily. Experiments show that TANE is fast in practice. For benchmark databases the running times are improved by several orders of magnitude over previously published results. The algorithm is also applicable to much larger datasets than the previous methods


References in zbMATH (referenced in 31 articles , 1 standard article )

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  1. Combi, Carlo; Sala, Pietro: Mining approximate interval-based temporal dependencies (2016)
  2. Garnaud, Eve; Maabout, Sofian; Mosbah, Mohamed: Functional dependencies are helpful for partial materialization of data cubes (2015)
  3. Baixeries, Jaume; Kaytoue, Mehdi; Napoli, Amedeo: Characterizing functional dependencies in formal concept analysis with pattern structures (2014)
  4. Cambazard, Hadrien; O’Sullivan, Barry: Erratum to “Reformulating table constraints using functional dependencies---an application to explanation generation” (2010)
  5. Medina, Raoul; Nourine, Lhouari: Conditional functional dependencies: an FCA point of view (2010)
  6. de Marchi, Fabien; Lopes, Stéphane; Petit, Jean-Marc: Unary and n-ary inclusion dependency discovery in relational databases (2009) ioport
  7. Jaudoin, H.; Flouvat, F.; Petit, J.-M.; Toumani, F.: Towards a scalable query rewriting algorithm in presence of value constraints (2009)
  8. Marchi, Fabien De; Lopes, Stéphane; Petit, Jean-Marc: Unary and n-ary inclusion dependency discovery in relational databases (2009) ioport
  9. Medina, Raoul; Nourine, Lhouari: A unified hierarchy for functional dependencies, conditional functional dependencies and association rules (2009)
  10. Wolf, Garrett; Kalavagattu, Aravind; Khatri, Hemal; Balakrishnan, Raju; Chokshi, Bhaumik; Fan, Jianchun; Chen, Yi; Kambhampati; Subbarao: Query processing over incomplete autonomous databases: query rewriting using learned data dependencies (2009) ioport
  11. Cambazard, Hadrien; O’Sullivan, Barry: Reformulating table constraints using functional dependencies-an application to explanation generation (2008)
  12. Sánchez, Daniel; Serrano, José-María; Blanco, Ignacio; Martín-Bautista, Maria J.; Miranda, María Amparo Vila: Using association rules to mine for strong approximate dependencies. (2008) ioport
  13. Sánchez, Daniel; Serrano, José María; Blanco, Ignacio; Martín-Bautista, Maria Jose; Vila, María-Amparo: Using association rules to mine for strong approximate dependencies (2008) ioport
  14. Trinh, Thu: Using transversals for discovering XML functional dependencies (2008)
  15. Yao, Hong; Hamilton, Howard J.: Mining functional dependencies from data (2008) ioport
  16. Yao, Hong; Hamilton, Howard J.: Mining functional dependencies from data. (2008) ioport
  17. Yu, Cong; Jagadish, H.V.: XML schema refinement through redundancy detection and normalization (2008) ioport
  18. Wei, Mingzhen; Bai, Baojun; Sung, Andrew H.; Liu, Qingzhong; Wang, Jiachun; Cather, Martha E.: Predicting injection profiles using ANFIS (2007) ioport
  19. Baixeries, Jaume; Balcázar, José Luis: Characterization and Armstrong relations for degenerate multivalued dependencies using formal concept analysis (2005)
  20. Balcázar, José Luis; Baixeries, Jaume: Characterizations of multivalued dependencies and related expressions (2004)

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